graphs, | Math to Power Industry

Awesense

Tue, 24 Mar 2026 00:00:00 +0000

At Awesense, we’ve been building a platform for power grid digital twins with the goal of allowing easy access to and use of electrical grid data in order to build a myriad of applications and use cases for the decarbonized grid of the future, which will need to include more and more distributed energy resources (DERs) such as rooftop solar, batteries as well as electric vehicles (EVs) and still operate safely and efficiently.

Awesense has built a sandbox environment populated with synthetic but realistic data and exposing APIs on top of which such applications can be built. As such, what we are looking for is to create a collection of prototype applications demonstrating the power of the platform.

The current challenge involves building computational techniques for automatically detecting the presence of behind-the-meter electric vehicles and disaggregating their consumption from the overall household (meter) consumption.

Background

Energy disaggregation, also known as appliance disaggregation is a technique which is used to analyze and break down the energy consumption in a building or household into individual appliance-level energy usages. The goal is to identify and monitor the energy consumption of specific “appliances” without the need for additional metering or sensors on each device.

The process of energy disaggregation involves analyzing the overall power signal from a building or household and applying advanced algorithms and machine learning techniques to separate and attribute energy consumption to specific sources. One of these sources can be electric vehicles (EVs), particularly ones plugged-in directly into regular outlets. These are the focus of the proposed project.

The ability to perform energy disaggregation analytics holds significant importance for utilities and electricity distribution. By gaining granular insights into customers’ energy usage at a more granular level, utilities can develop targeted demand response programs, optimize load distribution, and enhance grid management. Energy disaggregation analytics enables utilities to identify peak demand periods, forecast load patterns, and make informed decisions regarding infrastructure investments.

Details

Electrical distribution grids are composed of grid elements of various types (e.g. power lines, transformers, switches, meters, SCADA devices, etc.) connected to each other in a network (graph) structure. A feeder is a set of distribution lines (often operating at medium voltage) that collectively transport power from a substation to a multitude of downstream loads. Certain grid elements like meters, SCADA devices, fixed or movable IoT sensors, and Distributed Energy Resources (DERs) produce time series data such as voltage, current, power, energy, battery state of charge, and other measurements.

In this project, the students will need to use the Awesense SQL or REST APIs to retrieve the necessary time series and grid structure information to determine (and visualize) which households (meters) likely have an electric vehicle, at what times is it plugged in and how much energy does it draw.

Additional information about the EV disaggregation use case can be found here.

Skillset

This work involves coding some analyses and visualizations on top of the data and APIs described above and devising an algorithm for the redistribution of load to optimize overall capacity. It would require good data wrangling, statistics and data visualization skills to design and then implement the best way to transform, aggregate and visualize the data, and good mathematical/algorithmic skills for the optimization piece. The data access APIs are in SQL form, so SQL querying skills would also be desirable. Alternatively, REST APIs can be made available. Beyond that, the tools and programming languages used to create the analyses, visualizations and algorithms would be up to the students. Typical ones we have used include BI tools like Power BI or Tableau and notebooking applications like Jupyter or Zeppelin combined with programming languages like Python or R.

Tool Access and Support

If the participants don’t have any electrical background, Awesense will teach enough of it to allow handling the given use case.

In addition to the previously mentioned SQL and REST APIs, the Awesense platform also comes with a web-based application (graphical user interface front-end) called TGI (True Grid Intelligence) that serves as a companion visual explorer for the data stored in the platform. The snapshot below shows a portion of the grid available in the synthetic dataset. An EV Charger is selected (map blue marker and highlighted row in the table) and its properties are shown in the left sidebar, along with an electrical flow time series chart. The SQL & REST APIs include functionality for retrieving all this information programmatically.

For the duration of the project, upon agreeing to a standard end-user licensing agreement, participants in this PIMS project will be given access to the sandbox environment, including TGI, the programmatic SQL and REST APIs and associated documentation, as well as access to a GitHub repository with sample SQL, REST and python code snippets in Jupyter notebooks, showcasing how to use the APIs.

A successful project will consist of an algorithm and a set of visuals answering the questions posed above for the sandbox dataset, accompanied by any BI tool files or notebook code used to produce them; Awesense permits and encourages the public sharing of these artifacts, as long as credit for the dataset and APIs is given to Awesense (e.g. by including a “Powered by Awesense” phrase and an Awesense website link; publishing the raw data retrieved from the sandbox is not permitted.

Important note : project participants will be given individual access credentials, and they should not share with anyone else (including not among themselves) nor cache/save them in publicly posted files.

Awesense

Sun, 05 May 2024 00:00:00 +0000

The current challenge involves building an application for optimizing the distribution of load (consumption) across the grid so it is balanced instead of overloaded in some areas and underloaded in others.

Background

The ongoing shift from carbon-heavy energy sources to electrical power has led to significant constraints on the capacity of the existing power distribution infrastructure, particularly at the medium-voltage (MV) level. As demands for electric power grow and previously small single-digit megawatt loads evolve into tens of megawatts, the strain on existing conductor lines, transformers, and HV/MV substations increases. Reinforcing or expanding infrastructure is both costly and time-consuming, and utilities must look for faster and more cost-effective ways to adapt to this new energy landscape.

Against this backdrop, the fundamental problem is to ensure that the MV grid can handle higher power demands without exceeding capacity limits. With more consumption points being connected and each demanding greater power, the grid approaches its maximum permissible load. Utilities, therefore, need to find ways to redistribute or reroute loads in order to alleviate tress on overloaded segments, maintain reliability, and defer or avoid major infrastructure upgrades.

Utilities can alleviate capacity constraints by selectively redistributing and rerouting loads within the MV grid, often by adjusting or reassigning segments of the grid among lines that have available capacity. Another option is to strategically connect line segments at carefully chosen locations. This approach can also involve taking advantage of overlapping conductor routes to form more balanced feeders, effectively reducing the load on certain lines while preventing overload conditions and thereby deferring costly infrastructure expansions—all without compromising service reliability.

Details

In this project, the students will need to use the Awesense SQL or REST APIs to retrieve the necessary time series and grid structure information to determine and visualize which parts of the grid are closest to capacity or in danger of over-capacity should additional load be added, and then devise an algorithm that identifies the best places where load can be shifted or swapped between feeders in order to accommodate more overall capacity. Because load (consumption) fluctuates over time, the problem has a temporal dimension that needs to be taken into account, as the magnitude of available capacity may vary with the time of day, week, or year.

Additional information about the grid capacity optimization use case can be found here. The diagrams below show potential types of re-routes/swaps of lines.

Skillset

This work involves coding some analyses and visualizations on top of the data and APIs described above and devising an algorithm for the redistribution of load to optimize overall capacity. It would require good data wrangling, statistics and data visualization skills to design and then implement the best way to transform, aggregate and visualize the data, and good mathematical/algorithmic skills for the optimization piece. The data access APIs are in SQL form, so SQL querying skills would also be required. Alternatively, REST APIs can be made available. Beyond that, the tools and programming languages used to create the analyses, visualizations and algorithms would be up to the students. Typical ones we have used include BI tools like Power BI or Tableau and notebooking applications like Jupyter or Zeppelin combined with programming languages like Python or R.

Tool Access and Support

If the participants don’t have any electrical background, Awesense will teach enough of it to allow handling the given use case.

For the duration of the project, upon agreeing to a standard end-user licensing agreement, participants in this PIMS project will be given access to the sandbox environment, including TGI, the programmatic SQL or REST APIs and associated documentation, as well as access to a GitHub repository with sample SQL, REST and python code snippets in Jupyter notebooks, showcasing how to use the APIs.

A successful project will consist of an algorithm and a set of visuals answering the questions posed above for the sandbox dataset, accompanied by any BI tool files or notebook code used to produce them; Awesense permits and encourages the public sharing of these artifacts, as long as credit for the dataset and APIs is given to Awesense (e.g. by including a “Powered by Awesense” phrase and an Awesense website link); publishing the raw data retrieved from the sandbox is not permitted.

Important note: project participants will be given individual access credentials, and they should not share with anyone else (including not among themselves) nor cache/save them in publicly posted files.

IOTO

Tue, 24 Mar 2026 00:00:00 +0000

Overview

We have controlled vocabularies and topic structures that are used to index and understand large bodies of text. We want to better understand how text is clustered around known structured topics so that unknown topics can be identified in texts and added to our controlled vocabularies and topic structures.

Background

Goverlytics^® seeks to produce low-dimensional representations of legislative activity to: 1) make politics accessible to a broader public; and to 2) increase focus on policy goals. The model for Goverlytics^® is sports analytics, which has transformed the way in which sports are understood and consumed. Goverlytics^® analyzes data generated during legislative sessions: attendance, documents, transcripts, vote tallies, audio and video recordings.

Analytics in sports first began with measurement of what could be easily measured – goals (of course!), strokes, hits, etc. By distilling all that goes on during the activity into a few dimensions that allow for quantification and comparison, analytics helps to explain and so increase comprehension and engagement. Increasingly complex measurements are being engineered from ever larger datasets to enhance predictions and decision-making. Both short-term outcomes and strategies that may be decided in game, and for long-term considerations such as player health are at stake.

Challenge

In some cases, Goverlytics^® has to start creating statistics for legislative sessions from simple audio tracks. Audio is transcribed into words of a language. Then the language words (and concatenations of them) are binned into topic discourse, by means language models and topic classifications. Finally, topic classifications are used to index parts of the legislative activity that are likely to be interesting for a broader public. This process is akin to the distillation of a sporting match into a highlights reel or abbreviated match summary e.g. What topics were discussed the most? Who talked about those topics? Were there any significant new topics, or was voting and discussion about previously known topics? Were there significant outliers? Smash hits?

Because legislative sessions can go on for hours with very little information of predictive or decision-making value, it can be costly to process raw data to reach insight. The challenge is to find shorter paths to interesting bits of discourse. Can methods from signal analysis or related mathematical fields be used to more efficiently signpost insight into legislative data? Unsupervised learning techniques may provide some guidance. However, a successful solution will reveal what in the legislative activity is deserving of attention from a policy point of view, ether by connecting with a known policy ontology (such as comparative agendas codebook, or by surfacing issues that should be connected to a known ontology.

Data

At a minimum, APIs covering topic data for various legislative leagues (Canada, BC, Alberta, etc.) will be made available to the M2PI team. These APIs reliably serve data concerning legislative ‘players’ and their topic-related interventions over a number of legislative sessions. Corresponding audio will also be supplied.

Further datasets concerning elections, voting, and financial data may be made available – depending time available, which legislative leagues the M2PI team elects to study, and how they choose to analyse.

Finance data are available from OECD, Statistics Canada, and legislative ’leagues’ themselves.
Topics are standardized along Comparative Agendas Project (CAP) lines
Charts of accounts for finance overlap topic categories, but do not correspond exactly.
Voting data for bills and motions may be available for certain legislatures.
Audio files are available for whatever legislative level is chosen for study by the M2PI team.

University of Victoria

Tue, 24 Mar 2026 00:00:00 +0000

Overview

The goal of this project is to develop an interface between quantum and classical (binary) computing systems for climate modeling.

Climate models are large, complex computer programs made up of multiple components that represent different parts of the Earth system, such as the atmosphere, oceans, cryosphere, and vegetation. Each component is typically developed as a separate code, and many of these are further divided into sub-modules.

For example, atmospheric models usually include a dynamics module—often called the dynamical core—and one or more physics modules. The dynamical core is based on systematic discretization methods (such as finite differences, finite volumes, or spectral methods) to solve the equations of motion. In contrast, the physics modules represent processes that are not explicitly resolved by the dynamical core. These processes occur at spatial or temporal scales smaller than the model’s grid resolution and include phenomena such as radiation, phase changes of water and associated latent heat transfer, turbulence, and convection.

Because resolving these small-scale processes directly is computationally expensive, they are typically approximated using heuristic models based on ad hoc closure assumptions.

Although quantum computing is advancing rapidly, it is not yet practical to implement all components of climate models on quantum systems. Moreover, existing classical codes for dynamical cores are well established and highly reliable. However, if a quantum algorithm can be developed for specific sub-grid processes that is both efficient and accurate, it could be integrated with a classical dynamical core to create a hybrid quantum–classical modeling framework.

As a proof of concept, this project proposes to couple a simple convection model with a toy climate model developed by Khouider et al. (2010). The convection model, known as the Stochastic Multicloud Model (SMCM), is a Markov model that describes the area fractions of three cloud types.

In Khouider et al. (2010), the SMCM is coupled with a set of ordinary differential equations (ODEs) that describe the vertical profiles of temperature and moisture, assuming horizontal homogeneity (i.e., no spatial derivatives). More recently, Ueno and Miura (2025) developed a quantum implementation of the SMCM component alone.

This project aims to integrate the quantum SMCM code of Ueno and Miura (2025) with the ODE-based system used in Khouider et al. (2010), which serves as a simplified dynamical core. This integration will act as a demonstration of a hybrid quantum–classical climate modeling approach.

As a possible extension, the quantum SMCM code could also be applied to machine learning tasks. For example, it could be used to generate sample paths for a synthetic likelihood algorithm to calibrate the SMCM using radar data (Sevilla and Khouider, unpublished work).

References

Kazumasa Ueno, Hiroaki Miura, Quantum Algorithm for a Stochastic Multicloud Model, SOLA, 2025, Vol. 21, pp. 43-50, Publication Date 2025/01/22, [Early Release] Publication Date 2024/12/11, Online ISSN 1349-6476, https://doi.org/10.2151/sola.2025-006.
Khouider, B., J. Biello, and A. J. Majda, 2010: A stochastic multicloud model for tropical convection. Commun. Math. Sci., 8, 187–216.